Data Science News Flash: 08-22-2019

The latest data science articles - algorithmically curated, ranked, and summarized just for you.

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Future of data science: 5 factors shaping the field


  • Data science is relevant and important to any business that is churning out high volumes of data, which has lead to the rapid growth of artificial intelligence (AI) and machine learning adoption, said Ryohei Fujimaki, founder and CEO of dotData, a leading company focused on data science automation for the enterprise.
  • Some of the top skills necessary for data scientists include coding, big data analysis, statistics, machine learning, natural language processing, data manipulation, exploratory data analysis, and more, reported TechRepublic's Alison DeNisco Rayome.
  • To help the enterprise prepare for the future of data science, Fujimaki outlined the following five key factors shaping the data science industry.
  • In order to accelerate data science projects and reduce failures, CIOs and CDOs must focus on improving the quality of data and in providing data to data science teams that is relevant to projects at hand and is actionable .
  • The solution is to continue to accelerate hiring, while also looking at alternative means of accelerating the data science process and democratizing access to data science for other skilled professionals in areas like BI and analytics.

A set of skills for Data Scientist…


  • The well known Venn Diagram below clearly shows how Data Science is a combination of Math and statistics, computer skills and some expertise in the area in question.
  • The data scientist programming skills covers his ability to deal with large volumes of data, extract data from various sources (web scrapping, api calls…) working with real-time data, cloud computing, unstructured data, debugging, proficient in using the terminal and version control.
  • Matched with hacking or software engineering skills, statistics conducts to the machine learning world, in areas like artificial intelligence, high-frequency trading, spam detection, fraud detection, etc.
  • Dependent on the level of knowledge and vulgarization of data in the company, the data scientist role is to educate people on data, on better practice in terms of data sharing and data awareness.
  • A data scientist should try to develop more than one non-technical skills to succeed in his career (for example, if he wants to move from a data analyst to data manager or Business Intelligence Manager) or to sell his services.

Data Analysis and it’s types


  • The key to drawing real value from it is to understand the different data analysis techniques which will determine how successful your data-driven decisions are.
  • Where diagnostic analysis helps us to find the pattern in a well-organized data using descriptive analysis, here predictive makes use of both the types and try to make logical predictions of the outcome of events.
  • Prescriptive analysis is the frontier of data analysis, it takes into the insights of all the previous analyses to determine the best course of action for a problem or decision.
  • According to various surveys to find the most accepted type of data analytics tools, for 2016 Global Data and Analytics Survey.
  • These surveys, gives us a better picture of the need of the Data Analytics tools and their importance in decision making in our industries.



  • Modern healthcare equipment generates a lot of health data, and this is where Big Data applications can help.
  • The tools for big data analytics and data science for healthcare may vary, but the need drives the technologies to evolve.
  • The purpose of the Healthcare Data Scientist is to make sense of all the incoming data and make the insights usable by the rest of their colleagues — researchers, doctors, and others.
  • That we have clarified what the Data Scientist’s role is let’s take a look at how the Healthcare system can benefit from data science.
  • Data governance practices also help to abide by the HIPAA — the Health Insurance Portability and Accountability Act of 1996, which requires pseudonymization of certain aspects of data and thorough monitoring of its use.

Data Science Skills Study 2019: By AIM & Imarticus Learning


  • Our latest Data Science Skills Study 2019 by Analytics India Magazine and Imarticus Learning takes a deeper look into the key trends related to tools and technologies deployed across the sectors and how companies are staying ahead of the pack.
  • As analytics and machine learning reaches deeper into operations, there is significant disruption at the workplace with data scientists and data analysts dabbling with newer tools.
  • With a plethora of data analytics tools available online, we asked data scientists if they were willing to use open-sourced tools at work.
  • GPUs come in handy, especially when data scientists have to work with areas of deep learning such as back-propagation, Natural Language Processing (NLP) and Artificial Neural Networks (ANN), among others, which are advancing gradually and are already catching up with traditional technologies.
  • As the analytics industry grows at an astronomical speed, more professionals are expected to segue into the Data Science and Analytics sector.

Cerebras CEO talks about the big implications for machine learning in company’s big chip


  • What you may not know is that the WSE and the systems it makes possible have some fascinating implications for deep learning forms of AI, beyond merely speeding up computations.
  • Second, the concept of "sparsity," of dealing with individual data points rather than a group or "batch," may take a more central role in deep learning.
  • Cerebras's "wafer-scale engine," left, compared to a top-of-the-line graphics processing unit from Nvidia, the "V100," popular in deep learning training.
  • As each training data point exits an activation unit in one layer of the network, the network parameters have transformed that data point from what it was when it entered the network.
  • As a result, the distribution of data is transformed by the successive layers of the network, so much so that it becomes different from the original statistics of the training data.

AI is breathing new life into the intelligence community


  • As those threats change, and so does technology, artificial intelligence is poised to disrupt — but also refresh — intelligence agencies, giving them a new advantage in sifting through a mounting barrage of data to stay ahead of threats.
  • Ahead, a future—as in all knowledge industries—still coming into view but shaped by the powerful and potentially disruptive effects of artificial intelligence, big data, and machine learning”.
  • Top CIA officials — who produce the vast majority of the President’s Daily Brief, the IC’s most valuable document — have higher hopes for AI applications in intelligence analysis.
  • The Intelligence Advanced Research Projects Agency, the intelligence community’s own futuristic DARPA-like research arm, has a keen eye on AI.
  • ODNI stresses that the key to AI’s success in the intelligence community is sharing data across agencies, sharing the shallow talent pool and sharing capabilities as they are developed.

Advanced Topics in Neural Networks


  • A smaller learning rate results in slower learning, and whilst convergence is possible, it may only occur after an inordinate number of epochs, which is computationally inefficient.
  • The only way to achieve better results is to use a dynamic learning rate that tries to leverage the spatial and temporal variations in the optimal learning rate.
  • Whilst this is a nice idea, it tells us nothing about what learning rate scheme we should set and the magnitude of these learning rates.
  • After 30 iterations, the learning rate scheduler resets the learning rate to the same value as epoch 1, and then the learning rate scheduler repeats the same exponentially decay.
  • This idea is similar to the cyclical learning rate except for the learning rate graph typically looks more like a sawtooth wave rather than something symmetric and cyclic.

Integration of Artificial Intelligence and Machine Learning with Analytics


  • Without AI, analytics is a tool to comprehend what has happened dependent on data you have chosen and questions for which you have arranged answers.
  • With machine learning however, your analytics tool would perceive patterns of activity and alarm you just when something was really unusual.
  • As these examples show, AI and machine learning combined with analytics have the ability to genuinely enable advertisers to accomplish their most eager objectives.
  • As per their research, three out of four companies executing AI and machine learning have increased their sales of new products and services by more than 10%.
  • One should put resources into data scientists who have aptitudes focused around AI and machine learning to build your applications; system engineers who guarantee the proper foundation is set up to support those applications; solution architects who manage enterprise implementation; and business consultants who comprehend one of a kind factors within the data and the business value that will be derived from the application.

A Data Science Leader’s Perspective on Getting Value from AI Workloads


  • In cases where data scientists have to work with extremely large data sets or extremely fast data streams such as clickstreams or business transactions, GPU provides more benefits.
  • Keeping in mind the changing AI workload requirements, silicon providers are building an end-to-end hardware and software solutions to enable data scientists to gain more value from data.
  • As data science and algorithms continue to shift, AI tech major Intel has invested in hardware that keeps pace with more complex models and also delivers more inference at the edge.
  • Intel also engaged with popular deep learning frameworks like TensorFlow* and MXNet* to deliver more optimizations and performance as the software continues to evolve along with the AI landscape.
  • Some of the major advancements that can be seen from hardware innovations are in automating data science and explaining the results of machine learning better.

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